Python Machine Learning Advanced

1. NLP & Sentiment Analysis

Environment Setup & NLP Fundamentals

• VS Code environment configuration, NLP libraries installation
• Tokenization, stopword removal, stemming, lemmatization
• Text representation with Bag of Words and TF-IDF

Sentiment Analysis Project

• Logistic Regression for sentiment classification
• Data splitting, model evaluation metrics (accuracy, precision, recall, confusion matrix)

2. Recommendation Systems

Collaborative Filtering

• User-based and item-based filtering
• Cosine similarity for personalized recommendations

Content-Based Movie Recommender

• Vectorizing text using TF-IDF
• Implementing content similarity algorithms

3. Flask App for Recommendations

Building an ML-Powered Web App

• Flask basics and web serving
• Developing a recommendation system Flask app

4. Forecasting & Deep Learning

Time Series with Facebook Prophet

• Trend forecasting and visualization (e.g., market prices)

Deep Learning with PyTorch

• CNN basics, image classification using the CIFAR-10 dataset
• Model training, accuracy assessment, and confusion matrix interpretation

5. Object Detection

Real-Time Object Detection with YOLO

• Image detection and labeling with pretrained models
• Adapting YOLO models to video streams and real-time webcam input